Academic cheating is common, but little is known about its early emergence. It was examined among Chinese second to sixth graders (N = 2094; 53% boys, collected between 2018 and 2019) using a machine learning approach. Overall, 25.74% reported having cheated, which was predicted by the best machine learning algorithm (Random Forest) at a mean…

A quick and effective learning assessment is needed to evaluate the learning process. Many tools currently offer automatic assessment for subjective and objective questions; however, there is no such free tool that provides plagiarism detection among students for subjective questions in a learning management system (LMS). This article aims to…

Automatic grading models are valued for the time and effort saved during the instruction of large student bodies. Especially with the increasing digitization of education and interest in large-scale standardized testing, the popularity of automatic grading has risen to the point where commercial solutions are widely available and used. However,…

Research into "gaming the system" behavior in intelligent tutoring systems (ITS) has been around for almost two decades, and detection has been developed for many ITSs. Machine learning models can detect this behavior in both real-time and in historical data. However, intelligent tutoring system designs often change over time, in terms…

Academic dishonesty poses a challenge for the online and campus-based learning environment where technology and assessment at a distance may encourage easy and innovative ways of cheating. The purpose of this quantitative study was to assess campus-based and online students' attitudes and perceptions toward academic dishonesty. Data were collected…

Prevalence of contract cheating and outsourcing through organised methods has received interest in research studies aiming to determine the most suitable strategies to reduce the problem. Few studies have presented an international approach or tested which variables could be correlated with contract cheating. As a result, strategies to reduce…

Dishonest academic behavior (DAB) by students in Chinese higher education institutions has become a significant concern. However, the related study of academic dishonesty in mainland China is very limited. This study fills this gap by examining the theory of planned behavior and its three extended versions, validating the effectiveness of…

Producers and consumers of test scores are increasingly concerned about fraudulent behavior before and during the test. There exist several statistical or psychometric methods for detecting fraudulent behavior on tests. This paper provides a review of the Bayesian approaches among them. Four hitherto-unpublished real data examples are provided to…

Producers and consumers of test scores are increasingly concerned about fraudulent behavior before and during the test. There exist several statistical or psychometric methods for detecting fraudulent behavior on tests. This paper provides a review of the Bayesian approaches among them. Four hitherto-unpublished real data examples are provided to…

Background: It is generally accepted that well-established classroom rules prevent problem behaviour, while also supporting students' achievement gains. Yet, there might be considerable variability in students' underlying motives to comply or refrain from complying with classroom rules, with some students adhering to them because they fully accept…

When morality is important and central to individuals' identities (moral identity), it may heighten their sense of responsibility to behave in moral ways. Although research has linked moral identity to various moral actions, research has yet to demonstrate the association between moral identity and individuals' consistent moral choices, despite…

University students' latent attitudes toward capitalism were quantified and used to predict self-reported cheating behaviors. Results suggest that the relationship between student academic dishonesty and attitudes toward capitalism are complex. Students indicating a strong degree of risk aversion are less likely to report cheating behaviors.…

The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for…

The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…

The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…